Hugging Face's logo

qualcomm
/
Whisper-Medium-En

Automatic Speech Recognition
PyTorch
foundation
android
Model card Files Community

Whisper-Medium-En: Optimized for Mobile Deployment

Automatic speech recognition (ASR) model for English transcription as well as translation

OpenAI’s Whisper ASR (Automatic Speech Recognition) model is a state-of-the-art system designed for transcribing spoken language into written text. It exhibits robust performance in realistic, noisy environments, making it highly reliable for real-world applications. Specifically, it excels in long-form transcription, capable of accurately transcribing audio clips up to 30 seconds long. Time to the first token is the encoder's latency, while time to each additional token is decoder's latency, where we assume a mean decoded length specified below.

This model is an implementation of Whisper-Medium-En found here.

This repository provides scripts to run Whisper-Medium-En on Qualcomm® devices. More details on model performance across various devices, can be found here.

Model Details

  • Model Type: Speech recognition
  • Model Stats:
    • Model checkpoint: medium.en
    • Input resolution: 80x3000 (30 seconds audio)
    • Mean decoded sequence length: 224 tokens
    • Number of parameters: 769 M
    • Model size (WhisperEncoder): 769 MB
    • Model size (WhisperDecoder): 726 MB
Model Device Chipset Target Runtime Inference Time (ms) Peak Memory Range (MB) Precision Primary Compute Unit Target Model
WhisperDecoder Samsung Galaxy S23 Snapdragon® 8 Gen 2 QNN 38.334 ms 160 - 167 MB FP16 NPU Whisper-Medium-En.so
WhisperDecoder Samsung Galaxy S24 Snapdragon® 8 Gen 3 QNN 30.874 ms 161 - 178 MB FP16 NPU Whisper-Medium-En.so
WhisperDecoder Snapdragon 8 Elite QRD Snapdragon® 8 Elite QNN 30.408 ms 141 - 544 MB FP16 NPU Use Export Script
WhisperDecoder SA7255P ADP SA7255P QNN 212.509 ms 154 - 163 MB FP16 NPU Use Export Script
WhisperDecoder SA8255 (Proxy) SA8255P Proxy QNN 38.11 ms 162 - 165 MB FP16 NPU Use Export Script
WhisperDecoder SA8295P ADP SA8295P QNN 39.889 ms 160 - 170 MB FP16 NPU Use Export Script
WhisperDecoder SA8650 (Proxy) SA8650P Proxy QNN 38.703 ms 162 - 165 MB FP16 NPU Use Export Script
WhisperDecoder SA8775P ADP SA8775P QNN 40.488 ms 161 - 169 MB FP16 NPU Use Export Script
WhisperDecoder QCS8275 (Proxy) QCS8275 Proxy QNN 212.509 ms 154 - 163 MB FP16 NPU Use Export Script
WhisperDecoder QCS8550 (Proxy) QCS8550 Proxy QNN 38.285 ms 162 - 165 MB FP16 NPU Use Export Script
WhisperDecoder QCS9075 (Proxy) QCS9075 Proxy QNN 40.488 ms 161 - 169 MB FP16 NPU Use Export Script
WhisperDecoder Snapdragon X Elite CRD Snapdragon® X Elite QNN 33.066 ms 162 - 162 MB FP16 NPU Use Export Script
WhisperEncoder Samsung Galaxy S23 Snapdragon® 8 Gen 2 QNN 1812.235 ms 1 - 8 MB FP16 NPU Whisper-Medium-En.so
WhisperEncoder Samsung Galaxy S24 Snapdragon® 8 Gen 3 QNN 1342.833 ms 1 - 17 MB FP16 NPU Whisper-Medium-En.so
WhisperEncoder SA7255P ADP SA7255P QNN 9877.481 ms 0 - 7 MB FP16 NPU Use Export Script
WhisperEncoder SA8295P ADP SA8295P QNN 1808.045 ms 0 - 10 MB FP16 NPU Use Export Script
WhisperEncoder SA8650 (Proxy) SA8650P Proxy QNN 1798.114 ms 1 - 3 MB FP16 NPU Use Export Script
WhisperEncoder SA8775P ADP SA8775P QNN 1678.664 ms 0 - 7 MB FP16 NPU Use Export Script
WhisperEncoder QCS8275 (Proxy) QCS8275 Proxy QNN 9877.481 ms 0 - 7 MB FP16 NPU Use Export Script
WhisperEncoder QCS8550 (Proxy) QCS8550 Proxy QNN 1770.444 ms 1 - 3 MB FP16 NPU Use Export Script
WhisperEncoder QCS9075 (Proxy) QCS9075 Proxy QNN 1678.664 ms 0 - 7 MB FP16 NPU Use Export Script
WhisperEncoder Snapdragon X Elite CRD Snapdragon® X Elite QNN 1356.093 ms 0 - 0 MB FP16 NPU Use Export Script

Installation

Install the package via pip:

pip install "qai-hub-models[whisper-medium-en]"

Configure Qualcomm® AI Hub to run this model on a cloud-hosted device

Sign-in to Qualcomm® AI Hub with your Qualcomm® ID. Once signed in navigate to Account -> Settings -> API Token.

With this API token, you can configure your client to run models on the cloud hosted devices.

qai-hub configure --api_token API_TOKEN

Navigate to docs for more information.

Demo off target

The package contains a simple end-to-end demo that downloads pre-trained weights and runs this model on a sample input.

python -m qai_hub_models.models.whisper_medium_en.demo

The above demo runs a reference implementation of pre-processing, model inference, and post processing.

NOTE: If you want running in a Jupyter Notebook or Google Colab like environment, please add the following to your cell (instead of the above).

%run -m qai_hub_models.models.whisper_medium_en.demo

Run model on a cloud-hosted device

In addition to the demo, you can also run the model on a cloud-hosted Qualcomm® device. This script does the following:

  • Performance check on-device on a cloud-hosted device
  • Downloads compiled assets that can be deployed on-device for Android.
  • Accuracy check between PyTorch and on-device outputs.
python -m qai_hub_models.models.whisper_medium_en.export

Profiling Results
------------------------------------------------------------
WhisperDecoder
Device                          : Samsung Galaxy S23 (13)
Runtime                         : QNN                    
Estimated inference time (ms)   : 38.3                   
Estimated peak memory usage (MB): [160, 167]             
Total # Ops                     : 5747                   
Compute Unit(s)                 : NPU (5747 ops)         

------------------------------------------------------------
WhisperEncoder
Device                          : Samsung Galaxy S23 (13)
Runtime                         : QNN                    
Estimated inference time (ms)   : 1812.2                 
Estimated peak memory usage (MB): [1, 8]                 
Total # Ops                     : 3213                   
Compute Unit(s)                 : NPU (3213 ops)

How does this work?

This export script leverages Qualcomm® AI Hub to optimize, validate, and deploy this model on-device. Lets go through each step below in detail:

Step 1: Compile model for on-device deployment

To compile a PyTorch model for on-device deployment, we first trace the model in memory using the jit.trace and then call the submit_compile_job API.

import torch

import qai_hub as hub
from qai_hub_models.models.whisper_medium_en import Model

# Load the model
model = Model.from_pretrained()
decoder_model = model.decoder
encoder_model = model.encoder

# Device
device = hub.Device("Samsung Galaxy S23")

# Trace model
decoder_input_shape = decoder_model.get_input_spec()
decoder_sample_inputs = decoder_model.sample_inputs()

traced_decoder_model = torch.jit.trace(decoder_model, [torch.tensor(data[0]) for _, data in decoder_sample_inputs.items()])

# Compile model on a specific device
decoder_compile_job = hub.submit_compile_job(
    model=traced_decoder_model ,
    device=device,
    input_specs=decoder_model.get_input_spec(),
)

# Get target model to run on-device
decoder_target_model = decoder_compile_job.get_target_model()
# Trace model
encoder_input_shape = encoder_model.get_input_spec()
encoder_sample_inputs = encoder_model.sample_inputs()

traced_encoder_model = torch.jit.trace(encoder_model, [torch.tensor(data[0]) for _, data in encoder_sample_inputs.items()])

# Compile model on a specific device
encoder_compile_job = hub.submit_compile_job(
    model=traced_encoder_model ,
    device=device,
    input_specs=encoder_model.get_input_spec(),
)

# Get target model to run on-device
encoder_target_model = encoder_compile_job.get_target_model()

Step 2: Performance profiling on cloud-hosted device

After compiling models from step 1. Models can be profiled model on-device using the target_model. Note that this scripts runs the model on a device automatically provisioned in the cloud. Once the job is submitted, you can navigate to a provided job URL to view a variety of on-device performance metrics.

decoder_profile_job = hub.submit_profile_job(
    model=decoder_target_model,
    device=device,
)
encoder_profile_job = hub.submit_profile_job(
    model=encoder_target_model,
    device=device,
)

Step 3: Verify on-device accuracy

To verify the accuracy of the model on-device, you can run on-device inference on sample input data on the same cloud hosted device.

decoder_input_data = decoder_model.sample_inputs()
decoder_inference_job = hub.submit_inference_job(
    model=decoder_target_model,
    device=device,
    inputs=decoder_input_data,
)
decoder_inference_job.download_output_data()
encoder_input_data = encoder_model.sample_inputs()
encoder_inference_job = hub.submit_inference_job(
    model=encoder_target_model,
    device=device,
    inputs=encoder_input_data,
)
encoder_inference_job.download_output_data()

With the output of the model, you can compute like PSNR, relative errors or spot check the output with expected output.

Note: This on-device profiling and inference requires access to Qualcomm® AI Hub. Sign up for access.

Deploying compiled model to Android

The models can be deployed using multiple runtimes:

  • TensorFlow Lite (.tflite export): This tutorial provides a guide to deploy the .tflite model in an Android application.

  • QNN (.so export ): This sample app provides instructions on how to use the .so shared library in an Android application.

View on Qualcomm® AI Hub

Get more details on Whisper-Medium-En's performance across various devices here. Explore all available models on Qualcomm® AI Hub

License

  • The license for the original implementation of Whisper-Medium-En can be found here.
  • The license for the compiled assets for on-device deployment can be found here

References

Community

Downloads last month

-

Downloads are not tracked for this model. How to track
Inference Providers NEW
Automatic Speech Recognition
This model is not currently available via any of the supported Inference Providers.
The model cannot be deployed to the HF Inference API: The HF Inference API does not support automatic-speech-recognition models for pytorch library.